Papers
Dual Space Gradient Descent for Online Learning
Trung Le, Tu Nguyen, Vu Nguyen et al.
Regret of Queueing Bandits
Subhashini Krishnasamy, Rajat Sen, Ramesh Johari et al.
How Deep is the Feature Analysis underlying Rapid Visual Categorization?
Sven Eberhardt, Jonah G Cader, Thomas Serre
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Weiran Wang, Jialei Wang, Dan Garber et al.
Learning Structured Sparsity in Deep Neural Networks
Wei Wen, Chunpeng Wu, Yandan Wang et al.
Statistical Inference for Pairwise Graphical Models Using Score Matching
Ming Yu, Mladen Kolar, Varun Gupta
Tracking the Best Expert in Non-stationary Stochastic Environments
Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Daniel Ritchie, Anna Thomas, Pat Hanrahan et al.
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Jack Rae, Jonathan J Hunt, Ivo Danihelka et al.
Spatiotemporal Residual Networks for Video Action Recognition
Christoph Feichtenhofer, Axel Pinz, Richard Wildes
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat, Walid Krichene, Claire Tomlin et al.
The non-convex Burer-Monteiro approach works on smooth semidefinite programs
Nicolas Boumal, Vlad Voroninski, Afonso Bandeira
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Yizhe Zhang, Xiangyu Wang, Changyou Chen et al.
Designing smoothing functions for improved worst-case competitive ratio in online optimization
Reza Eghbali, Maryam Fazel
Bayesian optimization for automated model selection
Gustavo Malkomes, Charles Schaff, Roman Garnett
Disease Trajectory Maps
Peter Schulam, Raman Arora
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
Travis Monk, Cristina Savin, Jörg Lücke
Synthesis of MCMC and Belief Propagation
Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
Pruning Random Forests for Prediction on a Budget
Feng Nan, Joseph Wang, Venkatesh Saligrama
Completely random measures for modelling block-structured sparse networks
Tue Herlau, Mikkel N Schmidt, Morten Mørup
Diffusion-Convolutional Neural Networks
James Atwood, Don Towsley
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan et al.
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Chi Jin, Sham M. Kakade, Praneeth Netrapalli